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Record W2024928075 · doi:10.4271/2014-01-0992

Edge Formability and Material Characterization of Hot-Rolled Multiphase Steels

2014· article· en· W2024928075 on OpenAlexafffund
N. Pathak, Cliff Butcher, Michael J. Worswick, E.M. Bellhouse, Jeff Chen Yi Gao

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2014
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaAUTO21 Network of Centres of ExcellenceArcelorMittal
KeywordsFormabilityMaterials scienceEnhanced Data Rates for GSM EvolutionCharacterization (materials science)MetallurgyHot rolledComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

New innovations in the field of advanced high strength steels (AHSS) have led to the development of steels with improved stretch-flangeability known as hot-rolled multi-phase (HR) steels. To understand the performance of HR steels, hole expansion tests were conducted on five prototype HR steels and compared with their commercial dual-phase (DP) steel equivalent. A variety of hole edge conditions were considered to study the influence of the shear-affected-zone (SAZ), the surface roughness at the sheared edge and the shear burr orientation. The microstructure of each material was characterized and discussed in relation to its formability for the different edge conditions. It was observed that the bainitic-ferrite microstructure of the HR steels showed superior formability during sheared edge stretching compared to commercial dual-phase steels.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.234
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2014
Admission routes2
Has abstractyes

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